Skip to main content
Log in

A-optimal convolutional neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a novel data representation-classification model learning algorithm. The model is a convolutional neural network (CNN), and we learn its parameters to achieve A-optimality. The input multi-instance data are represented by a CNN model, and then classified by a linear classification model. The A-optimality of a classification model is measured by the trace of the covariance matrix of the model parameter vector. To achieve the A-optimality of the CNN model, we minimize the classification errors and a regularization term to present the classification model parameter as a function of the CNN filter parameters, and minimize its trace of the covariance matrix. We show that the minimization problem can be solved easily by transferring it to another coupled minimization problem. In the experiments over benchmark data sets of molecular, image, and seismic waveform, we show the advantages of the proposed A-optimal CNN model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Andrews S, Tsochantaridis I, Hofmann T (2002) Support vector machines for multiple-instance learning. Adv Neural Inf Process Syst 15:561–568

    Google Scholar 

  2. Cai X, Song B (2016) Combining inconsistent textures using convolutional neural networks. J Vis Commun Image Represent 40:366–375

    Article  Google Scholar 

  3. Chen Y, Bi J, Wang JZ (2006) Miles: Multiple-instance learning via embedded instance selection. IEEE Trans Pattern Anal Mach Intell 28(12):1931–1947

    Article  Google Scholar 

  4. Ciresan DC, Meier U, Gambardella LM, Schmidhube J (2011) Convolutional neural network committees for handwritten character classification. In: 2011 international conference on document analysis and recognition. IEEE, pp 1135–1139

  5. Dietterich TG, Lathrop RH, Lozano-Pérez T (1997) Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(1):31–71

    Article  Google Scholar 

  6. Ebrahimi M, Suen C, Ormandjieva O (2016) Detecting predatory conversations in social media by deep convolutional neural networks. Digit Investig 18:33–49

    Article  Google Scholar 

  7. Fan J, Liang RZ (2016) Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison. Neural Comput Appl 1–11

  8. Fan X, Malone B, Yuan C (2014) Finding optimal Bayesian network structures with constraints learned from data. In: Proceedings of the 30th conference on uncertainty in artificial intelligence (UAI-2014), pp 200–209

  9. Fan X, Tang K (2010) Enhanced maximum auc linear classifier. In: 2010 seventh international conference on fuzzy systems and knowledge discovery (FSKD), vol 4. IEEE, pp 1540–1544

  10. Fan X, Tang K, Weise T (2011) Margin-based over-sampling method for learning from imbalanced datasets. In: Proceedings of the 15th Pacific-Asia conference on knowledge discovery and data mining (PAKDD-2011). Springer, Berlin, pp 309–320

    Chapter  Google Scholar 

  11. Fan X, Yuan C (2015) An improved lower bound for Bayesian network structure learning. In: Proceedings of the 29th AAAI conference on artificial intelligence (AAAI-2015), pp 3526–3532

  12. Fan X, Yuan C, Malone B (2014) Tightening bounds for Bayesian network structure learning. In: Proceedings of the 28th AAAI conference on artificial intelligence (AAAI-2014), pp 2439–2445

  13. He X, Zhang C, Zhang L, Li X (2016) A-optimal projection for image representation. IEEE Trans Pattern Anal Mach Intell 38(5):1009–1015

    Article  Google Scholar 

  14. Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, Van de Walle R, Van Hoecke S (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345

    Article  Google Scholar 

  15. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  17. Leng B, Liu Y, Yu K, Zhang X, Xiong Z (2016) 3d object understanding with 3d convolutional neural networks. Inf Sci 366:188–201

    Article  MathSciNet  Google Scholar 

  18. Li P, Bu J, Chen C, Wang C, Cai D (2013) Subspace learning via locally constrained a-optimal nonnegative projection. Neurocomputing 115:49–62

    Article  Google Scholar 

  19. Li Q, Zhou X, Gu A, Li Z, Liang RZ (2016) Nuclear norm regularized convolutional Max Pos@Top machine. Neural Comput Appl

  20. Li W, Liu H, Yang P, Xie W (2016) Supporting regularized logistic regression privately and efficiently. PloS ONE 11(6):e0156,479

    Article  Google Scholar 

  21. Li W, Mo W, Zhang X, Lu Y, Squiers JJ, Sellke EW, Fan W, DiMaio JM, Thatcher JE (2015) Burn injury diagnostic imaging device’s accuracy improved by outlier detection and removal. In: SPIE defense+ security. International Society for Optics and Photonics, pp 947,206–947,206

  22. Li W, Mo W, Zhang X, Squiers JJ, Lu Y, Sellke EW, Fan W, DiMaio JM, Thatcher JE (2015) Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging. J Biomed Opt 20(12):121,305–121,305

    Article  Google Scholar 

  23. Liang RZ, Liang G, Li W, Li Q, Wang JJY (2016) Learning convolutional neural network to maximize pos@ top performance measure. arXiv preprint arXiv:1609.08417

  24. Liang RZ, Shi L, Wang H, Meng J, Wang JJY, Sun Q, Gu Y (2016) Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE

  25. Liang RZ, Xie W, Li W, Wang H, Wang JJY, Taylor L (2016) A novel transfer learning method based on common space mapping and weighted domain matching. In: 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI)

  26. Lin F, Wang J, Zhang N, Xiahou J, McDonald N (2016) Multi-kernel learning for multivariate performance measures optimization. Neural Comput Appl 1–13

  27. Lin Q, Chen L, Li S, Wu X (2010) A high-resolution fiber optic accelerometer based on intracavity phase-generated carrier (pgc) modulation. Meas Sci Technol 22(1):015,303

    Article  Google Scholar 

  28. Lin X, Liu J, Kang X (2016) Audio recapture detection with convolutional neural networks. IEEE Trans Multimedia 18(8):1480–1487

    Article  Google Scholar 

  29. Liu H, Yang Z, Wu Z, Li X (2012) A-optimal nonnegative projection for image representation. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1592–1599

  30. Liu X, Wang J, Yin M, Edwards B, Xu P (2015) Supervised learning of sparse context reconstruction coefficients for data representation and classification. Neural Comput Appl 1–9

  31. Matsugu M, Mori K, Mitari Y, Kaneda Y (2003) Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16(5):555–559

    Article  Google Scholar 

  32. Qiu C, Shen H, Chen L (2015) Towards green cloud computing: demand allocation and pricing policies for cloud service brokerage. In: 2015 IEEE international conference on big data (big data). IEEE, pp 203–212

  33. Sikora M, Wróbel Ł (2010) Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines. Arch Min Sci 55(1):91–114

    Google Scholar 

  34. Wang H, Wang J (2014) An effective image representation method using kernel classification. In: 2014 IEEE 26th international conference on tools with artificial intelligence (ICTAI 2014), pp 853–858

  35. Wang J, Wang H, Zhou Y, McDonald N (2015) Multiple kernel multivariate performance learning using cutting plane algorithm. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1870–1875

  36. Wang J, Zhou Y, Duan K, Wang JJY, Bensmail H (2015) Supervised cross-modal factor analysis for multiple modal data classification. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1882–1888

  37. Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona P (2016) Action recognition from depth maps using deep convolutional neural networks. IEEE Trans Hum Mach Syst 46(4):498–509

    Article  Google Scholar 

  38. Xie W, Kantarcioglu M, Bush WS, Crawford D, Denny JC, Heatherly R, Malin BA (2014) Securema: protecting participant privacy in genetic association meta-analysis. Bioinformatics 30(23):3334–3341

    Article  Google Scholar 

  39. Yang W, Jin L, Tao D, Xie Z, Feng Z (2016) Dropsample: a new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten chinese character recognition. Pattern Recogn 58:190–203

    Article  Google Scholar 

  40. Yang Z, Liu H, Cai D, Wu Z (2016) A-optimal non-negative projection with hessian regularization. Neurocomputing 174:838–849

    Article  Google Scholar 

  41. Zhang Y, Daigle BJ, Cohen M, Petzold L (2015) A cure time model for joint prediction of outcome and time-to-outcome. In: 2015 IEEE international conference on data mining (ICDM). IEEE, pp 1117–1122

  42. Zhang Y, Wu TB, Daigle BJ, Cohen M, Petzold L (2016) Identification of disease states associated with coagulopathy in trauma. BMC Med Inform Decis Mak 16(1):124

    Article  Google Scholar 

  43. Zhong J, Yang B, Huang G, Zhong F, Chen Z (2016) Remote sensing image fusion with convolutional neural network. Sens Imaging 17(1):10. doi:10.1007/s11220-016-0135-6

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by China Scholarship Council (201507005063) and Key Laboratory of High-speed Railway Engineering (Southwest Jiaotong University), Ministry of Education.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zihong Yin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, Z., Kong, D., Shao, G. et al. A-optimal convolutional neural network. Neural Comput & Applic 30, 2295–2304 (2018). https://doi.org/10.1007/s00521-016-2783-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2783-9

Keywords

Navigation